To summarize the clinical experience of Professor LIU Jinmin in treatment for epilepsy. It is believed that main pathogenesis of epilepsy is yangming failure to close and vital activity loss control， so a therapeutic approach focused on restoring the closure of yangming and regaining vital activity was proposed for the treatment of epilepsy. For excess syndrome， the treatment focuses on draining excess and descending qi， promoting purgation and restoring spirit. When yangming dryness-heat predominates， the approach involves unblock the bowels and regulating the spirit， descending qi and reducing fire， with modified Chengqi Decoction （承气汤） as prescription； when yangming phlegm-fire predominates， the treatment focuses on clearing heat and resolving phlegm， calming mind and suppressing fright， with modified Qingxin Wendan Decoction （清心温胆汤） as prescription； when yangming blood stasis predominates， the approach involves breaking up blood stasis and promoting purgation， eliminating stasis and awakening the mind， with Taoren Chengqi Decoction （桃核承气汤） as prescription. For deficiency syndrome， the treatment emphasizes tonifying deficiency and raising qi， strengthening the stomach and nourishing the spirit. When center qi deficiency and sinking of clear qi of the nutrients from food， the approach involves replenishing and uplifting qi while nourishing vital activity， with modified Liujunzi Decoction （六君子汤） as prescription； when yin deficiency and fluid consumption， the treatment focuses on nourishing stomach and tonifying yin， promoting fluid production and calming the spirit， with modified Maimendong Decoction （麦门冬汤） combined with Yiwei Decoction （益胃汤） as prescriptions. In clinical situations of deficiency-excess complex， it is essential to distinguish the primary condition from the secondary， applying both supplementing and draining methods flexibly to achieve optimal treatment.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective The purpose of writing the"Expert consensus on the prevention and control of intracranial hypertension in adult critical illness"(here in after referred to as the"Consensus")aimed to standardize the nursing work related to the prevention and control of elevated intracranial pressure in adult critical illness,and prevent the occurrence of complications such as cerebral herniation.Methods Guided by evidence-based practice,domestic and foreign databases were searched for guidelines,expert consensuses,systematic evaluation,evidence summaries,and original research related to increased intracranial pressure.The search period is from database establishment to March 2024.The high-quality evidence and suggestions in the field was evaluated,extracted,and summarized to form a preliminary consensus.27 experts were invited to conduct 2 rounds of expert inquiry and 8 experts were invited to conduct 2 expert discussion meetings,to revise and improve the content of the initial draft,and to ultimately form a final consensus.Results The effective response rates for both rounds of inquiry questionnaires were 100％,with expert authority coefficients of 0.884,judgment coefficients of 0.964,and familiarity levels of 0.804.The Kendall harmony coefficients for 2 rounds of inquiry were 0.107 and 0.083(P＜0.01),respectively.The consensus includes 4 aspects,including identification,monitoring,prevention and control strategies,emergency treatment and care for increased intracranial pressure.Conclusion This"Consensus"has strong scientific validity and can provide reference basis for nurses to carry out prevention and control of intracranial pressure increase.

The purpose of this study was to enrich the genomic information and provide a basis for further development and utilization of Polygonatum kingianum. The fresh rhizome of P. kingianum was used as the experimental material, and the full-length transcriptome was sequenced by PacBio Sequel platform. The final measured polymerase reads were 1 120 485, with a total of 77.73 GB of data. In NR database, 41 864 homologous sequence alignments were aligned to 5 species; 40 506 were annotated in the KOG database and classified into 26 categories based on their functionality; 69 060 GO annotated 32 functional groups divided into 3 categories: cellular components, molecular functions, and biological processes; 45 779 annotations were added to 145 metabolic pathways in the KEGG database. Among them, there are 127 identified transcripts related to polysaccharide synthesis in the glycolysis/gluconeogenesis metabolic pathway, 144 in the starch and sucrose metabolic pathway, 85 in the amino acid sugar and nucleotide sugar metabolic pathway, and 69 in the fructose and mannose metabolic pathway. In addition, 1 781 transcription factors were detected, distributed in 37 transcription factor families; 180 293 SSR loci were detected, among which single base repeats accounted for the most, accounting for 30.48% of all base repeats, and at least five base repeats, accounting for only 0.14% of single base repeats. The results of this study provide important data supporting for enriching the genomic information of P. kingianum and exploring its effective biosynthetic pathway.

2-(4-Methylthiazol-5-yl) ethyl nitrate hydrochloride (W1302) is a nitro containing derivative of clomethiazole, which is a novel neuroprotective agent with both carbon monoxide (NO) donor and weak γ-aminobutyric acid type A (GABA_A) receptor allosteric regulatory excitatory effect. The current study used a rat model of transient middle cerebral arteryocclusion (tMCAO) brain injury to evaluate the therapeutic effect of W1302 on ischemic stroke and explore its potential mechanisms of action. This experiment has been reviewed and approved by the Laboratory Animal Management and Use Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences (ethical review forms No. 620, 632, 5013). The results showed that gavage administration of 1, 3, and 10 mg·kg^-1 of W1302 can significantly reduce the volume of cerebral infarction in rats with ischemia for 2 h and reperfusion for 24 h, and the therapeutic effect was better than that of 200 mg·kg^-1 of DL-3n-butylphthalide. W1302 significantly increased NO levels in blood and brain tissue. It increased cerebral blood flow and brain adenosine triphosphate (ATP) content after reperfusion, as well as, inhibited the expressions of inflammatory factors tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) in brain tissue. The time window of W1302 was between 120-180 min after ischemia. These research results demonstrated that W1302 could increase NO release, dilate blood vessels, and increase cerebral blood flow, improve energy supply to brain tissue and increase ATP levels, and inhibit neuro-inflammation, which playing the protective roles in tMCAO stroke model rats. This provides theoretical support for the clinical application of W1302 in the treatment of ischemic stroke.

Objective To analyze the characteristics of pattern elements and the distribution of patterns of femoral head necrosis, hip osteoarthritis, and hip rheumatoid arthritis, in order to provide a theoretical basis for the "different diseases with the same pattern" of chronic bone diseases. Methods A cross-sectional survey was conducted to select patients with femoral head necrosis Association Research Circulation Osseous Ⅰ-Ⅱ stages, hip osteoarthritis Kellgren & Lawrence Ⅰ-Ⅱ stages, and acute or subacute hip rheumatoid arthritis who visited the Minimally Invasive Arthrology Department, Traumatology Department, and Rheumatology and Immunology Department of Beijing University of Chinese Medicine Third Affiliated Hospital from June 2020 to June 2022. The " case report form - traditional Chinese medicine pattern manifestation scale" previously developed by our team was used to collect the pattern manifestations, which were included into an Excel 2020 spreadsheet to establish a database. SPSS 20.0 software was used for factor analysis and cluster analysis to extract pattern element information, such as disease nature and location, in order to summarize the characteristics of pattern elements, the distribution of patterns, and the similarities and differences of the three kinds of early hip joint diseases.Results A total of 410 patients were included, including 150 patients with femoral head necrosis, 160 patients with hip osteoarthritis, and 100 patients with hip rheumatoid arthritis. The pattern elements of the disease nature of femoral head necrosis include phlegm (dampness), blood stasis, yang deficiency, essence deficiency, and qi deficiency. The pattern types were initially divided into four categories: syndrome of meridian obstruction (43.33％), syndrome of phlegm and blood stasis blocking collaterals (38.00％), syndrome of liver and kidney deficiency (12.00％), and syndrome of kidney essence deficiency (6.67％). The pattern elements of the disease nature of hip osteoarthritis include phlegm (dampness), blood stasis, qi deficiency, essence deficiency, yang deficiency, and cold (dampness). The pattern types were preliminarily divided into five categories: syndrome of spleen and kidney deficiency (37.50％), syndrome of meridian obstruction (26.87％), syndrome of cold and dampness obstruction (18.75％), syndrome of phlegm and blood stasis obstruction (9.38％), and syndrome of liver and kidney deficiency (7.50％). The pattern elements of the disease nature of hip rheumatoid arthritis include phlegm (dampness), blood stasis, qi deficiency, yin and yang deficiency, cold (dampness), and essence deficiency. The pattern types were preliminarily divided into four categories: syndrome of phlegm and blood stasis obstruction (34.00％), syndrome of cold and dampness obstruction (28.00％), syndrome of blood stasis blocking collaterals (23.00％), and syndrome of liver and kidney deficiency (15.00％). Overall, the top five pattern manifestations of the three kinds of hip joint diseases were hip joint pain (96.59％), tenderness (93.90％), fixed pain (87.56％), heavy joints (85.37％), and sourness of lower limbs (75.37％). The pattern elements of the disease nature include phlegm (dampness), blood stasis, qi deficiency, etc. The pattern types were preliminarily divided into five categories: syndrome of phlegm stasis blocking collaterals (33.17％), syndrome of meridian obstruction (31.95％), syndrome of cold dampness obstruction (21.46％), syndrome of liver and kidney deficiency (7.32％), and syndrome of spleen and kidney deficiency (6.10％). There were 21 similar pattern manifestations in the three kinds of early hip joint diseases, with blood stasis and spleen deficiency being the main pattern.Conclusion The common pattern characteristics of three kinds of early hip joint diseases are spleen deficiency and blood stasis. In addition, femoral head necrosis is accompanied with phlegm-dampness pattern, hip osteoarthritis is accompanied with kidney deficiency and phlegm-dampness pattern, hip rheumatoid arthritis is accompanied with kidney deficiency and cold-dampness pattern.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.